CN107274298A - A kind of agricultural product price fluctuation method for early warning and system - Google Patents
A kind of agricultural product price fluctuation method for early warning and system Download PDFInfo
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Abstract
The present invention discloses a kind of agricultural product price fluctuation method for early warning and system.Method includes：Historical price fluctuation to the agricultural product of species to be measured in region to be measured is sampled, and obtains sample data；Sample data is analyzed using Density Estimator method, the probability density function of agricultural product price fluctuation is obtained；The price fluctuation value in the range of the corresponding default price fluctuation of each police's degree is obtained from sample data；Least square computing is carried out to price fluctuation value, the corresponding price fluctuation point position probability of each alert degree is obtained；According to the price fluctuation scope of probability density function police's degree different with price fluctuation point position determine the probability；The agricultural product price undulating value of species to be measured in region to be measured and price fluctuation scope are contrasted, when agricultural product price undulating value is in the range of price fluctuation, the early warning of correspondence police's degree is sent.Method and system disclosed by the invention, can carry out accurate early warning to the price fluctuation of agricultural product, improve the objectivity of agricultural product price fluction analysis.
Description
Technical Field
The invention relates to the technical field of agricultural product price monitoring, in particular to an agricultural product price fluctuation early warning method and system.
Background
The agricultural products are in an industrial form combining natural reproduction and social reproduction, and play a vital role in the development of agriculture and rural economy, so that the analysis of the price fluctuation of the agricultural products has very important significance in the macroscopic regulation and control of governments and the production strategy appointed by farmers.
The price of the agricultural product is influenced by the production space layout, the consumption time distribution of the product and the lag of market signals and policy effects, so that the price fluctuation of the agricultural product is a more complex change process. At present, the means for analyzing the price fluctuation of agricultural products at home and abroad is mostly judged directly by experts. However, the means of judgment by experts is limited by the experience of experts, and the analysis result is subjective.
Disclosure of Invention
The invention aims to provide an agricultural product price fluctuation early warning method and system, which can accurately early warn the price fluctuation of agricultural products and improve the objectivity of agricultural product price fluctuation analysis.
In order to achieve the purpose, the invention provides the following scheme:
an agricultural product price fluctuation early warning method comprises the following steps:
sampling historical price fluctuation of agricultural products of a to-be-detected type in a to-be-detected area to obtain sample data;
analyzing the sample data by using a nuclear density estimation method to obtain a probability density function of agricultural product price fluctuation; the probability density function is a function reflecting a probability of a possible occurrence of a price fluctuation value of the agricultural product;
acquiring price fluctuation values in a preset price fluctuation range corresponding to each alarm degree from the sample data; the alarm degree is in direct proportion to the price fluctuation value of the agricultural product;
performing least square operation on the price fluctuation value to obtain price fluctuation quantile probability corresponding to each alarm degree; the price fluctuation quantile probability is smaller than the price fluctuation value corresponding to the alarm degree;
determining price fluctuation ranges with different degrees of alarm according to the probability density function and the price fluctuation quantile probability;
and comparing the price fluctuation value of the agricultural product of the type to be detected in the area to be detected with the price fluctuation range, and sending out an early warning corresponding to the alarm degree when the price fluctuation value of the agricultural product is in the price fluctuation range.
Optionally, the analyzing the sample data by using a kernel density estimation method to obtain a probability density function of price fluctuation of an agricultural product includes:
taking a Gaussian kernel function as a kernel function of the kernel density estimation method, and analyzing the sample data to obtain a probability density function;
selecting the window width of the probability density function, and determining the optimal window width; the optimal window width is the window width which enables the deviation of the probability density function relative to the sample data to be minimum; the window width is a parameter of the probability density function.
The invention also discloses an agricultural product price fluctuation early warning system, which comprises:
the sampling module is used for sampling the historical price fluctuation of the agricultural products of the type to be detected in the region to be detected to obtain sample data;
the nuclear density estimation module is used for analyzing the sample data by using a nuclear density estimation method to obtain a probability density function of agricultural product price fluctuation; the probability density function is a function reflecting a probability of a possible occurrence of a price fluctuation value of the agricultural product;
the system comprises a sample data acquisition module, a price fluctuation value acquisition module and a price fluctuation value acquisition module, wherein the sample data acquisition module is used for acquiring price fluctuation values in a preset price fluctuation range corresponding to various polices from the sample data; the alarm degree is in direct proportion to the price fluctuation value of the agricultural product;
the quantile calculation module is used for performing least square operation on the price fluctuation value to obtain price fluctuation quantile probability corresponding to each alarm degree; the price fluctuation quantile probability is smaller than the price fluctuation value corresponding to the alarm degree;
the alarm degree range determining module is used for determining price fluctuation ranges with different alarm degrees according to the probability density function and the price fluctuation quantile probability;
and the early warning module is used for comparing the price fluctuation value of the agricultural product of the type to be detected in the area to be detected with the price fluctuation range, and sending out early warning corresponding to the alarm degree when the price fluctuation value of the agricultural product is in the price fluctuation range.
Optionally, the kernel density estimation module specifically includes:
a probability density function determining unit, configured to analyze the sample data to obtain a probability density function, with a gaussian kernel function as a kernel function of the kernel density estimation method;
the window width selection unit is used for selecting the window width of the probability density function and determining the optimal window width; the optimal window width is the window width which enables the deviation of the probability density function relative to the sample data to be minimum; the window width is a parameter of the probability density function.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the agricultural product price fluctuation early warning method and system, the historical price fluctuation of the agricultural products is sampled and analyzed, and the price fluctuation rule of specific agricultural product types in a specific area is accurately obtained, so that the price fluctuation ranges with different alarm degrees are determined, and the price fluctuation of the agricultural products can be accurately early warned. Meanwhile, the method and the system are analyzed according to regions and agricultural product types, so that the method and the system are suitable for price early warning of various agricultural products, have general applicability and pertinence, and can improve the accuracy while improving the application range of the method. The method is obtained through objective analysis based on historical data, and improves the objectivity of analysis of the price fluctuation of the agricultural products compared with the existing means of directly judging through experts.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flowchart of a first embodiment of a price fluctuation warning method for agricultural products according to the present invention;
fig. 2 is a system structure diagram of the agricultural product price fluctuation early warning system according to the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The first embodiment is as follows:
fig. 1 is a flowchart of a first method of an agricultural product price fluctuation warning method according to an embodiment of the present invention.
Referring to fig. 1, the agricultural product price fluctuation early warning method includes:
step 101: sampling historical price fluctuation of agricultural products of a to-be-detected type in a to-be-detected area to obtain sample data;
step 102: analyzing the sample data by using a nuclear density estimation method to obtain a probability density function of agricultural product price fluctuation; the probability density function is a function reflecting a probability of a possible occurrence of a price fluctuation value of the agricultural product; the method specifically comprises the following steps:
taking a Gaussian kernel function as a kernel function of the kernel density estimation method, and analyzing the sample data to obtain a probability density function;
selecting the window width of the probability density function, and determining the optimal window width; the optimal window width is the window width which enables the deviation of the probability density function relative to the sample data to be minimum; the window width is a parameter of the probability density function;
step 103: acquiring price fluctuation values in a preset price fluctuation range corresponding to each alarm degree from the sample data; the alarm degree is in direct proportion to the price fluctuation value of the agricultural product;
step 104: performing least square operation on the price fluctuation value to obtain price fluctuation quantile probability corresponding to each alarm degree; the price fluctuation quantile probability is smaller than the price fluctuation value corresponding to the alarm degree;
step 105: determining price fluctuation ranges with different degrees of alarm according to the probability density function and the price fluctuation quantile probability;
step 106: and comparing the price fluctuation value of the agricultural product of the type to be detected in the area to be detected with the price fluctuation range, and sending out an early warning corresponding to the alarm degree when the price fluctuation value of the agricultural product is in the price fluctuation range.
According to the agricultural product price fluctuation early warning method, the historical price fluctuation of the agricultural products is sampled and analyzed, so that the price fluctuation rule of specific agricultural product types in a specific area is accurately obtained, the price fluctuation ranges with different alarm degrees are determined, and the price fluctuation of the agricultural products can be accurately early warned. Meanwhile, the method is analyzed according to regions and agricultural product types, so that the method is suitable for price early warning of various agricultural products, has general applicability and pertinence, and can improve the accuracy while improving the application range of the method. The method is obtained through objective analysis based on historical data, and improves the objectivity of analysis of the price fluctuation of the agricultural products compared with the existing means of directly judging through experts.
Example two:
an agricultural product price fluctuation early warning method comprises the following steps:
sampling historical price fluctuation of agricultural products of the type to be detected in the area to be detected to obtain sample data.
Analyzing the sample data by using a nuclear density estimation method to obtain a probability density function of agricultural product price fluctuation;
the probability density function is expressed as:
whereinThe sample data is a probability density function, n is the number of the sample data, and i is the serial number of the sample data; xiFor the ith sample data, K (x) is a kernel function, x is a variable of the probability density function, and h is a window width.
In this embodiment, a gaussian kernel function is selected as the kernel function, and then the kernel function k (x) is:
the deviation and error of the probability density function obtained by the kernel density estimation method change towards different directions along with the change of the window width h, when the window width h is too large, the estimation curve is over smooth, and when the window width h is too small, the estimation curve is under smooth, so that the probability density function is sensitive to the selection of the window width h.
In order to make the probability density function closer to the true function, the present embodiment uses the Mean Square Error (MSE) criterion to select the window width so that the probability density function is closer to the true function f (x). The mean square error criterion requires that the mean square error be minimal. The mean square error is calculated as:
whereinRepresenting a probability density functionThe variance of (a) is determined,representing the deviation of the probability density function from the true function f (x).In the expectation that the position of the target is not changed,the expectation is that. o (h)4) Is h4Is a small quantity of (a) to be infinitesimally small,is composed ofAn infinitesimal small amount of.
When calculating the window width, the present embodiment calculates the optimal window width by using the least square cross-validation method. The least square cross verification method does not need to make any assumption on the probability density function, and can obtain the optimal window width by directly starting from actual data. The least squares cross-validation method minimizes the window width at which the Integrated Squared Error (ISE) of the probability density function is minimized to an optimal window width. Namely:
whereinRepresents the window width, h, when the squared integral variance is minimaloptThe window width is optimal;
then
Wherein, XjFor the jth sample data。
The probability density function can be determined after the window width is determined by the method.
After the probability density function is calculated, calculating the price fluctuation range of the agricultural products corresponding to each police degree by adopting a p-quantile method, specifically:
acquiring price fluctuation values in a preset price fluctuation range corresponding to each alarm degree from the sample data; the alarm degree is in direct proportion to the price fluctuation value of the agricultural product;
performing least square operation on the price fluctuation value to obtain price fluctuation quantile probability corresponding to each alarm degree; the price fluctuation quantile probability is smaller than the price fluctuation value corresponding to the alarm degree.
The price fluctuation quantile probability is set by adopting a qualitative and quantitative combined method, and the following principles are mainly followed: one is the importance and comprehensiveness of economic implications. For example, the probability density functions of the fruit and vegetable products and the meat products are obviously different in distribution, so that different price fluctuation quantile probabilities are set for different types of agricultural products; meanwhile, as the yield of the region is reduced, the price fluctuation of the corresponding region is more obvious, so that the setting of the price fluctuation quantile probability of different regions is different. Second, the measure ability of the index. The response of individual polices to overall produce market price changes needs to be sensitive and reliable, yet relatively stable. Thirdly, the timeliness of the index, the index is not static, but is adjusted, supplemented and modified along with the time to meet the market early warning requirement.
Therefore, the price fluctuation quantile probability can be obtained by a least square method, namely:
wherein，p(m)Price fluctuation quantile probability, x, for the mth alertiA price fluctuation value in a preset price fluctuation range corresponding to the mth alarm degree; x is the number of(m)And the expected quantile corresponding to the price fluctuation quantile probability of the mth alarm degree.
The invention converts discontinuous sample data into continuous functions and calculates the price fluctuation quantile probability of each alarm degree by using the continuous functions, thereby accurately calculating the specific numerical value of the quantile corresponding to each price fluctuation quantile probability, ensuring that the price fluctuation range corresponding to each alarm degree is more accurate and improving the accuracy of early warning.
Calculating the price fluctuation quantile probability p corresponding to each alarm degree(m)Then, the quantile corresponding to each price fluctuation quantile probability can be calculated and obtained according to the probability density functionThe quantileThe price fluctuation value corresponding to the price fluctuation quantile probability in the probability density function; the specific calculation formula is as follows:
wherein,for price fluctuation quantile probability p(m)The corresponding quantile;
different alarm degrees correspond to different quantiles, and the price fluctuation range of each alarm degree can be obtained according to each quantile.
In this embodiment, four warning levels are provided, and the warning levels correspond to four warning lights with different colors respectively during warning. The colors of the four warning lamps are red, orange, yellow and blue respectively according to the degree of alarm from high to low. Wherein the blue light shows that the price fluctuation is slightly high, the yellow light shows that the price fluctuation is higher, the orange light shows that the price fluctuation is too high, and the red light shows that the price fluctuation is extremely high. When the price fluctuation value of the agricultural product is in the price fluctuation range corresponding to a certain degree of alarm, the early warning lamp with the corresponding color is lightened.
Example three:
fig. 2 is a system structure diagram of the agricultural product price fluctuation early warning system according to the embodiment of the invention.
Referring to fig. 2, the agricultural product price fluctuation early warning system includes:
the sampling module 201 is used for sampling the historical price fluctuation of the agricultural products of the type to be detected in the region to be detected to obtain sample data;
the kernel density estimation module 202 is configured to analyze the sample data by using a kernel density estimation method to obtain a probability density function of price fluctuation of the agricultural product; the probability density function is a function reflecting a probability of a possible occurrence of a price fluctuation value of the agricultural product;
the alert degree fluctuation value acquisition module 203 is configured to acquire a price fluctuation value within a preset price fluctuation range corresponding to each alert degree from the sample data; the alarm degree is in direct proportion to the price fluctuation value of the agricultural product;
a quantile calculation module 204, configured to perform a least square operation on the price fluctuation value to obtain a price fluctuation quantile probability corresponding to each of the polices; the price fluctuation quantile probability is smaller than the price fluctuation value corresponding to the alarm degree;
an alert range determining module 205, configured to determine price fluctuation ranges with different alerts according to the probability density function and the price fluctuation quantile probability;
the early warning module 206 is configured to compare a price fluctuation value of an agricultural product of a type to be detected in a region to be detected with the price fluctuation range, and send an early warning corresponding to the alarm degree when the price fluctuation value of the agricultural product is within the price fluctuation range.
The kernel density estimation module 202 specifically includes:
a probability density function determining unit, configured to analyze the sample data to obtain a probability density function, with a gaussian kernel function as a kernel function of the kernel density estimation method;
the window width selection unit is used for selecting the window width of the probability density function and determining the optimal window width; the optimal window width is the window width which enables the deviation of the probability density function relative to the sample data to be minimum; the window width is a parameter of the probability density function.
According to the agricultural product price fluctuation early warning system, the historical price fluctuation of the agricultural products is sampled and analyzed, so that the price fluctuation rule of specific agricultural product types in a specific area is accurately obtained, the price fluctuation ranges with different alarm degrees are determined, and the price fluctuation of the agricultural products can be accurately early warned. Meanwhile, the system is analyzed according to regions and agricultural product types, so that the system can be suitable for price early warning of various agricultural products, has general applicability and pertinence, and can improve the accuracy while improving the application range of the method. The method is obtained through objective analysis based on historical data, and improves the objectivity of analysis of the price fluctuation of the agricultural products compared with the existing means of directly judging through experts.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (4)
1. An agricultural product price fluctuation early warning method is characterized by comprising the following steps:
sampling historical price fluctuation of agricultural products of a to-be-detected type in a to-be-detected area to obtain sample data;
analyzing the sample data by using a nuclear density estimation method to obtain a probability density function of agricultural product price fluctuation; the probability density function is a function reflecting a probability of a possible occurrence of a price fluctuation value of the agricultural product;
acquiring price fluctuation values in a preset price fluctuation range corresponding to each alarm degree from the sample data; the alarm degree is in direct proportion to the price fluctuation value of the agricultural product;
performing least square operation on the price fluctuation value to obtain price fluctuation quantile probability corresponding to each alarm degree; the price fluctuation quantile probability is smaller than the price fluctuation value corresponding to the alarm degree;
determining price fluctuation ranges with different degrees of alarm according to the probability density function and the price fluctuation quantile probability;
and comparing the price fluctuation value of the agricultural product of the type to be detected in the area to be detected with the price fluctuation range, and sending out an early warning corresponding to the alarm degree when the price fluctuation value of the agricultural product is in the price fluctuation range.
2. The agricultural product price fluctuation early warning method according to claim 1, wherein the analyzing the sample data by using a kernel density estimation method to obtain a probability density function of agricultural product price fluctuation specifically comprises:
taking a Gaussian kernel function as a kernel function of the kernel density estimation method, and analyzing the sample data to obtain a probability density function;
selecting the window width of the probability density function, and determining the optimal window width; the optimal window width is the window width which enables the deviation of the probability density function relative to the sample data to be minimum; the window width is a parameter of the probability density function.
3. An agricultural product price fluctuation early warning system, comprising:
the sampling module is used for sampling the historical price fluctuation of the agricultural products of the type to be detected in the region to be detected to obtain sample data;
the nuclear density estimation module is used for analyzing the sample data by using a nuclear density estimation method to obtain a probability density function of agricultural product price fluctuation; the probability density function is a function reflecting a probability of a possible occurrence of a price fluctuation value of the agricultural product;
the system comprises a sample data acquisition module, a price fluctuation value acquisition module and a price fluctuation value acquisition module, wherein the sample data acquisition module is used for acquiring price fluctuation values in a preset price fluctuation range corresponding to various polices from the sample data; the alarm degree is in direct proportion to the price fluctuation value of the agricultural product;
the quantile calculation module is used for performing least square operation on the price fluctuation value to obtain price fluctuation quantile probability corresponding to each alarm degree; the price fluctuation quantile probability is smaller than the price fluctuation value corresponding to the alarm degree;
the alarm degree range determining module is used for determining price fluctuation ranges with different alarm degrees according to the probability density function and the price fluctuation quantile probability;
and the early warning module is used for comparing the price fluctuation value of the agricultural product of the type to be detected in the area to be detected with the price fluctuation range, and sending out early warning corresponding to the alarm degree when the price fluctuation value of the agricultural product is in the price fluctuation range.
4. The agricultural product price fluctuation early warning system according to claim 3, wherein the kernel density estimation module specifically comprises:
a probability density function determining unit, configured to analyze the sample data to obtain a probability density function, with a gaussian kernel function as a kernel function of the kernel density estimation method;
the window width selection unit is used for selecting the window width of the probability density function and determining the optimal window width; the optimal window width is the window width which enables the deviation of the probability density function relative to the sample data to be minimum; the window width is a parameter of the probability density function.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798482A (en) * | 2017-11-16 | 2018-03-13 | 中国农业科学院农业信息研究所 | A kind of market for farm products unusual fluctuations risk monitoring method and system |
| CN108846689A (en) * | 2018-05-07 | 2018-11-20 | 河南金凤电子科技有限公司 | A kind of food price monitoring and managing method, equipment and system |
| CN109978617A (en) * | 2019-03-22 | 2019-07-05 | 中国农业科学院农业信息研究所 | Agricultural product big data prediction technique, system, terminal and storage medium |
| CN109978616A (en) * | 2019-03-22 | 2019-07-05 | 中国农业科学院农业信息研究所 | Agricultural product data monitoring early warning system |
| CN110706078A (en) * | 2019-09-30 | 2020-01-17 | 中国农业科学院农业信息研究所 | Fresh agricultural product price abnormal fluctuation monitoring and early warning method and system |
| CN112100037A (en) * | 2020-09-25 | 2020-12-18 | 北京奇艺世纪科技有限公司 | Alarm level identification method and device, electronic equipment and storage medium |
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| CN107798482A (en) * | 2017-11-16 | 2018-03-13 | 中国农业科学院农业信息研究所 | A kind of market for farm products unusual fluctuations risk monitoring method and system |
| CN108846689A (en) * | 2018-05-07 | 2018-11-20 | 河南金凤电子科技有限公司 | A kind of food price monitoring and managing method, equipment and system |
| CN108846689B (en) * | 2018-05-07 | 2021-03-16 | 河南金凤电子科技有限公司 | Food price supervision method, equipment and system |
| CN109978617A (en) * | 2019-03-22 | 2019-07-05 | 中国农业科学院农业信息研究所 | Agricultural product big data prediction technique, system, terminal and storage medium |
| CN109978616A (en) * | 2019-03-22 | 2019-07-05 | 中国农业科学院农业信息研究所 | Agricultural product data monitoring early warning system |
| CN110706078A (en) * | 2019-09-30 | 2020-01-17 | 中国农业科学院农业信息研究所 | Fresh agricultural product price abnormal fluctuation monitoring and early warning method and system |
| CN112100037A (en) * | 2020-09-25 | 2020-12-18 | 北京奇艺世纪科技有限公司 | Alarm level identification method and device, electronic equipment and storage medium |
| CN112100037B (en) * | 2020-09-25 | 2023-07-25 | 北京奇艺世纪科技有限公司 | Alarm level identification method, device, electronic equipment and storage medium |
| CN112269912A (en) * | 2020-11-18 | 2021-01-26 | 布瑞克农业大数据科技集团有限公司 | Agricultural big data price early warning management system and method |
| CN112269912B (en) * | 2020-11-18 | 2021-07-27 | 布瑞克农业大数据科技集团有限公司 | Agricultural big data price early warning management system and method |
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Application publication date: 20171020 |